Information In Complex Networks Research Articles

Complex Network Analysis of Experimental EEG Signals for Decoding Brain Cognitive State

Depicting the relationship between brain cognitive state and task difficulty level constitutes a challenging problem of significant importance. In order to probe it, we design an experiment to gather EEG data from mental arithmetic task under different difficulty levels. We construct brain complex networks using a complex network method and information entropy theory. We then employ weighted clustering coefficient to characterize the networks generated from different brain cognitive states. The results show that with the increase in task difficulty level, the mean weighted clustering coefficients show a decrease. This is due to the lack of coordination of brain activity and the low efficiency of the network organization caused by the increase in task difficulty. In addition, we calculate the permutation entropy from the signals of each channel EEG signals to support the findings from our network analysis. These findings render our method particularly useful for depicting the relationship between brain cognitive state and difficulty level.

RETRACTED ARTICLE: Intelligent analysis framework for healthy environment spatial model of BIM horticultural therapy based on complex network information model

With the continuous development of modern life, people have paid more and more attention to the needs of healthy and comfortable outdoor activities. The traditional static recuperation mode has been impacted, and the modern dynamic leisure agricultural tourism health care mode is more and more popular. Horticultural therapy improves people’s cognitive, psychological and physiological functions and provides people with positive values through plant-related sensory activities and horticultural operations. Therefore, horticultural therapy has become a product of the idea that designers have been exploring new forms of landscape architecture, trying to create a more comfortable environment and maximize the benefits of people in landscape architecture. In recent years, the theoretical and empirical studies of complex networks provide an important means to reveal the complexity of complex systems. As a new and active field of scientific research, complex networks have been introduced into the empirical research of real-world networks for a long time. At present, more and more people pay attention to computer science, social science, biological science, management science and many other fields. This paper constructs a BIM horticultural therapy health environment analysis system based on complex network model. The experimental results show that the proposed method can effectively analyze the application of BIM horticultural therapy in healthy environment.

LSMD: A fast and robust local community detection starting from low degree nodes in social networks

Community detection is an appropriate approach for discovering and understanding the structure and hidden information in complex networks One of the most critical issues in the community detection problem is the low-time complexity of the algorithm while preserving the accuracy of the algorithm, which is important in large-scale networks such as social networks. Local community detection algorithms try to use local information and provide acceptable results in a reasonable time. This paper proposes a fast and accurate community detection algorithm based on local information for the community’s label assigning. In the proposed algorithm, local community detection is started from low degree nodes by label assigning in a multi-level diffusion way, called LSMD algorithm, with significant low time complexity. In the first phase of the LSMD algorithm, at first, the community’s label is assigned to the node with degree 1 and its direct neighbor and second level neighbors. In fact, we used this fact that people with a smaller number of neighbors are not likely to be connected to diverse communities. Next, a community label is respectively assigned for the nodes with degrees 2, 3, and so forth. In the second phase, initial communities are merged using a new simple and fast strategy as far as possible to form the final communities. Besides, there is no random nature in the algorithm, as well as the adjustable parameter. Therefore, the obtained results show meaningful stability for the LSMD. Experiments are performed on real-world and synthetic networks to evaluate the performance and accuracy of the proposed algorithm. The results show that the proposed algorithm significantly is more accurate than the other state-of-the-art algorithms. In addition, it substantially is faster than other local algorithms such as LPA, LCCD, NIBLPA, DA, RTLCD, Louvain, G-CN, Infomap, and ECES on large-scale networks.

Medical data science in rhinology: Background and implications for clinicians.

An important challenge of big data is using complex information networks to provide useful clinical information. Recently, machine learning, and particularly deep learning, has enabled rapid advances in clinical practice. The application of artificial intelligence (AI) and machine learning (ML) in rhinology is an increasingly relevant topic. We review the literature and provide a detailed overview of the recent advances in AI and ML as applied to rhinology. Also, we discuss both the significant benefits of this work as well as the challenges in the implementation and acceptance of these methods for clinical purposes. We aimed to identify and explain published studies on the use of AI and ML in rhinology based on PubMed, Scopus, and Google searches. The search string "nasal OR respiratory AND artificial intelligence OR machine learning" was used. Most of the studies covered areas of paranasal sinuses radiology, including allergic rhinitis, chronic rhinitis, computed tomography scans, and nasal cytology. Cluster analysis and convolutional neural networks (CNNs) were mainly used in studies related to rhinology. AI is increasingly affecting healthcare research, and ML technology has been used in studies of chronic rhinitis and allergic rhinitis, providing some exciting new research modalities. AI is especially useful when there is no conclusive evidence to aid decision making. ML can help doctors make clinical decisions, but it does not entirely replace doctors. However, when critically evaluating studies using this technique, rhinologists must take into account the limitations of its applications and use.

Link Prediction in Signed Social Networks: From Status Theory to Motif Families

Link prediction can discover missing information and evolution mechanism of complex networks, so a huge number of novel algorithms have been proposed recently. However, the existing link prediction algorithms for directed signed networks only depend on motifs that satisfy status theory, and other types of motifs are rarely taken into account. In this study, first we propose a link prediction method based on the number of edge-dependent motifs, and explain it by a naive Bayes model. Furthermore, we put forward a Signed Local Naive Bayes (SLNB) model based on two kinds of different motifs, which has higher prediction performance than only considering a single motif. Finally, we combine all the 3-node motifs to form a motif family, and use a machine learning framework for link prediction. The results show that motif families can greatly improve the performance of link prediction. Moreover, according to the correlation between these predictors, the intrinsic relationship between different motifs can be discovered, and the computational complexity of link prediction can be reduced after feature selection. Our research can not only improve the performance of link prediction, but also be helpful to uncover the evolutionary mechanism of signed social networks.

Deep Reinforcement Learning for Dynamic Uplink/Downlink Resource Allocation in High Mobility 5G HetNet

Recently, the 5G is widely deployed for supporting communications of high mobility nodes including train, vehicular and unmanned aerial vehicles (UAVs) largely emerged as the main components for constructing the wireless heterogeneous network (HetNet). To further improve the radio utilization, the Time Division Duplex (TDD) is considered to be the potential full-duplex communication technology in the high mobility 5G network. However, the high mobility of users leads to the high dynamic network traffic and unpredicted link state change. A new method to predict the dynamic traffic and channel condition and schedule the TDD configuration in real-time is essential for the high mobility environment. In this paper, we investigate the channel model in the high mobility and heterogeneous network and proposed a novel deep reinforcement learning based intelligent TDD configuration algorithm to dynamically allocate radio resources in an online manner. In the proposal, the deep neural network is employed to extract the features of the complex network information, and the dynamic Q-value iteration based reinforcement learning with experience replay memory mechanism is proposed to adaptively change TDD Up/Down-link ratio by evaluated rewards. The simulation results show that the proposal achieves significant network performance improvement in terms of both network throughput and packet loss rate, comparing with conventional TDD resource allocation algorithms.

Reconstructing irreducible links in temporal networks: which tool to choose depends on the network size

Filtering information in complex networks entails the process of removing interactions explained by a proper null hypothesis and retaining the remaining interactions, which form the backbone network. The reconstructed backbone network depends upon the accuracy and reliability of the available tools, which, in turn, are affected by the specific features of the available dataset. Here, we examine the performance of three approaches for the discovery of backbone networks, in the presence of heterogeneous, time-varying node properties. In addition to the recently proposed evolving activity driven model, we extend two existing approaches (the disparity filter and the temporal fitness model) to tackle time-varying phenomena. Our analysis focuses on the influence of the network size, which was previously shown to be a determining factor for the performance of the evolving activity driven model. Through mathematical and numerical analysis, we propose general guidelines for the use of these three approaches based on the available dataset. For small networks, the evolving temporal fitness model offers a more reasonable trade-off between the number of links assigned to the backbone network and the accuracy of their inference. The main limitation of this methodology lies in its computational cost, which becomes excessively high for large networks. In this case, the evolving activity driven model could be a valid substitute to the evolving temporal fitness model. If one seeks to minimize the number of links inaccurately included in the backbone network at the risk of dismissing many links that could belong to it, then the temporal disparity filter would be the approach-of-choice. Overall, our contribution expands the toolbox of network discovery in the technical literature and should help users in choosing the right network discovery instrument, depending on the problem considered.

On entropy measures of molecular graphs using topological indices

Topological indices as a molecular descriptors are important tools in (QSAR)/(QSPR) studies. The graph entropies with topological indices inspired by Shannon’s entropy concept become the information-theoretic quantities for measuring the structural information of chemical graphs and complex networks. The graph entropy measures are playing an important role in a variety of problem areas including, discrete mathematics, biology, and chemistry. Our contribution is to explore graph entropies that are based on a novel information functional, which is the number of vertices of different degree together with the number of edges between different degree vertices.In this paper, we study the chemical graph of crystal structure of titanium difluoride TiF2 and crystallographic structure of cuprite Cu2O. Also, we compute entropies of these structures by making a relation of degree based topological indices namely first redefined Zagreb index, second redefined Zagreb index, third redefined Zagreb index, fourth Atom Bond Connectivity index, fifth Geometric Arithmetic index, Sanskruti index with the help of information function.

Allometric Scaling of Mutual Information in Complex Networks: A Conceptual Framework and Empirical Approach

Complexity and information theory are two very valuable but distinct fields of research, yet sharing the same roots. Here, we develop a complexity framework inspired by the allometric scaling laws of living biological systems in order to evaluate the structural features of networks. This is done by aligning the fundamental building blocks of information theory (entropy and mutual information) with the core concepts in network science such as the preferential attachment and degree correlations. In doing so, we are able to articulate the meaning and significance of mutual information as a comparative analysis tool for network activity. When adapting and applying the framework to the specific context of the business ecosystem of Japanese firms, we are able to highlight the key structural differences and efficiency levels of the economic activities within each prefecture in Japan. Moreover, we propose a method to quantify the distance of an economic system to its efficient free market configuration by distinguishing and quantifying two particular types of mutual information, total and structural.

A box-covering Tsallis information dimension and non-extensive property of complex networks

Abstract In this article, a box-covering Tsallis information dimension is introduced, and the physical interpretation of this new dimension has been assigned. Moreover, based on the introduced parameter q → , a characterization of non-extensive networks is stated, allowing the classification according to super-extensive ( q → ≺ 1 ), sub-extensive ( q → ≻ 1 ) or extensive ( q → = 1 ). The experimental results on both synthetic and real complex networks shed light on the type of interaction of the boxes. The results support the conjecture that the box-covering Tsallis information dimension is a suitable and flexible measure of information of real complex networks that exhibit a rich structural diversity.

Public opinion analysis of complex network information of local similarity clustering based on intelligent fuzzy system

With the rise of the network society, as the mapping Internet space, the public opinion has become the most active way of expressing social public opinion. It gradually gets deeply involved in the development and change of various social phenomena, social problems and social events, and evolves into the real politics and public management. In this context, it is of great practical significance to explore the evolution process and laws of online public opinions and systematically analyze the influence mechanism in the evolution process of online public opinions. This paper comprehensively uses the modeling simulation, empirical analysis, fuzzy systems and other research methods, adopts the reasonable abstraction of the main behavior characteristics, behavior motives and network relations of network users, and then constructs the evolution model of network public opinion in the complex social network. Besides, from the new research perspective of network members and network relations of the dynamic interaction between the government, media and netizen, this paper makes an in-depth study on the influence mechanism of the dynamic evolution of online public opinion.

Extracting Network Structure for International and Malaysia Website via Random Walk

World Wide Web is an information retrieval system accessible via the Internet. Since all the web resources and documents are interlinks with hypertext links, it formed a huge and complex information network. Besides information, the web is also a primary tool for commercial, entertainment and connecting people around the world. Hence, studying its network topology will give us a better understanding of the sociology of content on the web as well as the possibility of predicting new emerging phenomena. In this paper, we construct networks by using random walk process that traverses the web at two popular websites, namely google.com (global) and mudah.my (local). We perform measurement such as degree distribution, diameter and average path length on the networks to determine various structural properties. We also analyse the network at the domain level to identify some top-level domains appearing in both networks in order to understand the connectivity of the web in different regions. Using centrality analysis, we also reveal some important and popular websites and domain from the networks.

Securing instruction interaction for hierarchical management

Hierarchical management is a typical approach to managing complex information networks (CINs). In hierarchical management, as the only avenue to control information storage, transmission and usage, instruction interaction is often exploited by attackers to threaten CINs security. Now it has become a major challenge to ensure the security of instruction interaction. To address this challenge, in this paper, we propose a security model to manage instruction interaction for the hierarchical management in CINs. First, considering instruction lifecycle, we design five basic instruction operations (i.e., instruction generation, distribution, decomposition, execution, and execution outcome feedback) and formally define their semantics. Then a series of security rules for the basic operations are proposed to monitor and control the instruction interactions. We prove that, through these rules, both controllability and confidentiality can be provided. Finally, case studies demonstrate the feasibility of the proposed model.

Learning epidemic threshold in complex networks by Convolutional Neural Network.

Deep learning has taken part in the competition since not long ago to learn and identify phase transitions in physical systems such as many-body quantum systems, whose underlying lattice structures are generally regular as they are in Euclidean space. Real networks have complex structural features that play a significant role in dynamics in them, and thus the structural and dynamical information of complex networks cannot be directly learned by existing neural network models. Here, we propose a novel and effective framework to learn the epidemic threshold in complex networks by combining the structural and dynamical information into the learning procedure. Considering the strong performance of learning in Euclidean space, the Convolutional Neural Network (CNN) is used, and, with the help of "confusion scheme," we can identify precisely the outbreak threshold of epidemic dynamics. To represent the high-dimensional network data set in Euclidean space for CNN, we reduce the dimensionality of a network by using graph representation learning algorithms and discretize the embedded space to convert it into an imagelike structure. We then creatively merge the nodal dynamical states with the structural embedding by multichannel images. In this manner, the proposed model can draw the conclusion from both structural and dynamical information. A large number of simulations show a great performance in both synthetic and empirical network data sets. Our end to end machine learning framework is robust and universally applicable to complex networks with arbitrary size and topology.

An interactive propagation model of multiple information in complex networks

The relationships between information are intertwined when they are spreading in the network simultaneously. In order to study the impact of different interactions between information on the propagation and find the characteristics of multiple information propagation, we introduce the concept of interaction factor to describe the different relationships between information. Then we model the multiple information propagation based on the Susceptible–Infective–Removed (SIR) model. Based on the proposed model, we set up the dynamic equations by a Markov chain method, and theoretically analyze the propagation threshold. Extensive experiments were conducted on two real datasets and an artificially generated network dataset. The results verify the propagation threshold and show that the different relationships between information have different impact on information propagation.

A local community detection algorithm based on internal force between nodes

Community structure is an important characteristic of complex networks. Uncovering communities in complex networks is currently a hot research topic in the field of network analysis. Local community detection algorithms based on seed-extension are widely used for addressing this problem because they excel in efficiency and effectiveness. Compared with global community detection methods, local methods can uncover communities without the integral structural information of complex networks. However, they still have quality and stability deficiencies in overlapping community detection. For this reason, a local community detection algorithm based on internal force between nodes is proposed. First, local degree central nodes and Jaccard coefficient are used to detect core members of communities as seeds in the network, thus guaranteeing that the selected seeds are central nodes of communities. Second, the node with maximum degree among seeds is pre-extended by the fitness function every time. Finally, the top k nodes with the best performance in pre-extension process are extended by the fitness function with internal force between nodes to obtain high-quality communities in the network. Experimental results on both real and artificial networks show that the proposed algorithm can uncover communities more accurately than all the comparison algorithms.

Quantifying complex network information based on communicability sequence entropy

Viewing the network system from the perspective of information, it is a key issue to construct a measure containsthe overall information of the network in the network information theory. The communicability sequence entropy basedon node pair communication capability becomes considered a candidate measure to quantify network information.In order to explore its ability to characterize the global information of the network, we first study the influencesof the topology of the synthetic networks on the communicability sequence entropy. The results show that networks with strong heterogeneity,strong degree-degree correlation and community structure have relatively smaller communicability sequence entropy.Moreover, by studying the communicability sequence entropy of some real networks and their correspondingrandomized network models, we find that the higher the order of the randomized network modelis, the smaller communicability sequence entropy is, and the closer it is to the real networkscommunicability sequence entropy. These results show that the communicability sequence entropyof the network is sensitive to the basic topology of the network, and tends to decrease with increasing the order degreeof the network. The conclusions provide evidence for the ability of the communicability sequence entropy to quantify the overall information of the network.

A P\xf3lya urn approach to information filtering in complex networks

The increasing availability of data demands for techniques to filter information in large complex networks of interactions. A number of approaches have been proposed to extract network backbones by assessing the statistical significance of links against null hypotheses of random interaction. Yet, it is well known that the growth of most real-world networks is non-random, as past interactions between nodes typically increase the likelihood of further interaction. Here, we propose a filtering methodology inspired by the Pólya urn, a combinatorial model driven by a self-reinforcement mechanism, which relies on a family of null hypotheses that can be calibrated to assess which links are statistically significant with respect to a given network’s own heterogeneity. We provide a full characterization of the filter, and show that it selects links based on a non-trivial interplay between their local importance and the importance of the nodes they belong to.

Towards the Identification of Student Learning Communities using Centrality

Emergence of universities towards “digital university” has already been present for some years. The use of digital is largely developed to ensure a good quality of education. Universities therefore use large-scale learning management systems to manage the interaction between learners and teachers. Teachers can provide online training and educational materials for students following their classes and courses, monitor their participation and evaluate their performance. Students can use interactive features such as discussion threads, videoconferences, and discussion forums. These online tools make it possible to create new social networks or connect online social interactions. This will allow us to understand the structure of this complex network and extract useful information. In this article, we report our research on the detection of student learning communities based on learner activity. We found that it is possible to group students in communities through their messages and response structures using standard community detection algorithms. Also, that their behaviours can be strongly correlated with their closest peers who belong to the same community.

Exploring the characteristics of acupoints in the treatment of stroke with complex network and point mutual information method

Exploring the characteristics of acupoints in the treatment of stroke with complex network and point mutual information method